Protein-Ligand Scoring with Convolutional Neural Networks.
J Chem Inf Model
; 57(4): 942-957, 2017 04 24.
Article
en En
| MEDLINE
| ID: mdl-28368587
ABSTRACT
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Proteínas
/
Redes Neurales de la Computación
/
Biología Computacional
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Chem Inf Model
Asunto de la revista:
INFORMATICA MEDICA
/
QUIMICA
Año:
2017
Tipo del documento:
Article